import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()

results_folder = 'mmvec_major_taxa_2'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'

table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()

	Propionibacteriaceae	Staphylococcus caprae or capitis	Staphylococcus epidermidis	Staphylococcus hominis	Other Staphylococci	Polyomavirus HPyV6	Polyomavirus HPyV7	Merkel Cell Polyomavirus	Malasseziaceae	Corynebacteriaceae	Micrococcaceae	Other families
featureid
X940001	0.111823	0.006616	0.050163	-0.001373	0.099477	0.048717	-0.015278	0.082663	-0.016479	0.391788	0.167430	0.036258
X940002	-0.037990	-0.158498	-0.256362	-0.062324	-0.022124	-0.070099	-0.090522	-0.089765	-0.051773	-0.057602	-0.087969	-0.121828
X940005	-0.035200	-0.181835	-0.301041	-0.328792	0.202456	0.600453	0.238876	0.140972	0.017801	0.009918	0.071340	-0.009364
X940007	0.445166	0.335722	0.224301	0.512379	0.381924	0.168751	0.260878	0.300112	0.413439	0.399129	0.329138	0.312755
X940010	0.302998	-0.218579	0.748987	0.499472	0.354388	0.560653	1.057405	0.859181	-0.107463	0.440688	0.758574	0.595320

table['Selected'] = np.isin(table.index,
                            ['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
                             'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
                             'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
                             'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
                             'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
                             'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
                             'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
                           )
table.sort_values('Selected', inplace=True)
sns.relplot(
    table,
    y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)

<seaborn.axisgrid.FacetGrid at 0x7f946274a490>

sns.pairplot(table, hue='Selected')

<seaborn.axisgrid.PairGrid at 0x7f9462548790>

for i in table.columns[:-1]:
    sns.displot(table, x=i, hue='Selected', multiple='stack')